In many real applications, the input data are naturally expressed as tensors, such as virtual metrology in semiconductor manufacturing, face recognition and gait recognition in computer vision, etc. In this paper, we propose a general optimization framework for dealing with tensor inputs. Most existing methods for supervised tensor learning use only rank-one weight tensors in the linear model and cannot readily incorporate domain knowledge. In our framework, we obtain the weight tensor in a hierarchical way — we first approximate it by a low-rank tensor, and then estimate the low-rank approximation using the prior knowledge from various sources, e.g., different domain experts. This is motivated by wafer quality prediction in semiconductor manufacturing. Furthermore, we propose an effective algorithm named H-MOTE for solving this framework, which is guaranteed to converge. The time complexity of H-MOTE is linear with respect to the number of examples as well as the size of the weight tensor. Experimental results show the superiority of H-MOTE over state-of-the-art techniques on both synthetic and real data sets.

In this paper, we consider the multi-task sparse learning problem under the assumption that the dimensionality diverges with the sample size. The traditional l1/l2 multi-task lasso does not enjoy the oracle property unless a rather strong condition is enforced. Inspired by adaptive lasso, we propose a multi-stage procedure, adaptive multi-task lasso, to simultaneously conduct model estimation and variable selection across different tasks. Motivated by adaptive elastic-net, we further propose the adaptive multi-task elastic-net by adding another quadratic penalty to address the problem of collinearity. When the number of tasks is fixed, under weak assumptions, we establish the asymptotic oracle property for the proposed adaptive multi-task sparse learning methods including both adaptive multi-task lasso and elastic-net. In addition to the desirable asymptotic property, we show by simulations that adaptive sparse learning methods also achieve much improved finite sample performance. As a case study, we apply adaptive multi-task elastic-net to a cognitive science problem, where one wants to discover a compact semantic basis for predicting fMRI images. We show that adaptive multi-task sparse learning methods achieve superior performance and provide some insights into how the brain represents meanings of words.

The number of patents filed each year has increased dramatically in recent years, raising concerns that patents of questionable validity are restricting the issuance of truly innovative patents. For this reason, there is a strong demand to develop an objective model to quantify patent quality and characterize the attributes that lead to higher-quality patents. In this paper, we develop a latent graphical model to infer patent quality from related measurements. In addition, we extract advanced lexical features via natural language processing techniques to capture the quality measures such as clarity of claims, originality, and importance of cited prior art. We demonstrate the effectiveness of our approach by validating its predictions with previous court decisions of litigated patents.

This paper discusses several techniques used in developing a parallel, production quality data mining application in Java.We started by developing three sequential versions of a product recommendation data mining application: (i) a Fortran 90 version used as a performance reference, (ii) a plain Java implementation that only uses the primitive array structures from the language, and (iii) a baseline Java implementation that uses our Array package for Java.This Array package provides parallelism at the level of individual Array and BLAS operations.Using this Array package, we also developed two parallel Java versions of the data mining application: one that relies entirely on the implicit parallelism provided by the Array package, and another that is explicitly parallel at the application level.We discuss the design of the Array package, as well as the design of the data mining application.We compare the trade-offs between performance and the abstraction level the different Java versions present to the application programmer.Our studies show that, although a plain Java implementation performs poorly, the Java implementation with the Array package is quite competitive in performance with Fortran.We achieve a single processor performance of 109 Mflops, or 91% of Fortran performance, on a 332 MHz PowerPC 604e processor.Both the implicitly and explicitly parallel forms of our Java implementations also parallelize well.On an SMP with four of those PowerPC processors, the implicitly parallel form achieves 290 Mflops with no effort from the application programmer, while the explicitly parallel form achieves 340 Mflops.1